Skip to main content
Graph
Search
fr
en
Login
Search
All
Categories
Concepts
Courses
Lectures
MOOCs
People
Quizes
Exercises
Publications
Startups
Units
Show all results for
Home
Lecture
Threshold Functions in Neural Networks
Graph Chatbot
Related lectures (36)
Linear Classification: Logistic Regression
Log in to Mediaspace to watch this video
Covers linear classification using logistic regression, regularization, and multiclass classification.
Optical Machine Learning: Harnessing Multiple Scattering for Efficiency
Log in to Mediaspace to watch this video
Explores the use of optics in machine learning, focusing on large-scale random matrix multiplication through multiple scattering of light.
Neural Networks: Training and Optimization
Log in to Mediaspace to watch this video
Explores neural network training, optimization, and environmental considerations, with insights into PCA and K-means clustering.
Feature Learning: Stability and Curse of Dimensionality
Log in to Mediaspace to watch this video
Explores how modern architectures beat the curse of dimensionality and the importance of stability in deep learning models.
Auto-encoder and GANs
Log in to Mediaspace to watch this video
Covers auto-encoders for data compression and GANs for data generation.
Statistical Learning: Fundamentals
Log in to Mediaspace to watch this video
Introduces the fundamentals of statistical learning, covering supervised learning, decision theory, risk minimization, and overfitting.
Neural Networks: Regression and Classification
Log in to Mediaspace to watch this video
Explores neural networks for regression and classification tasks, covering training, regularization, and practical examples.
Decomposition into Line Metrics: Example and Outlook
Log in to Mediaspace to watch this video
Covers the decomposition into line metrics, providing examples and discussing its implications.
Neural Network Approximation and Learning
Log in to Mediaspace to watch this video
Delves into neural network approximation, supervised learning, challenges in high-dimensional learning, and deep learning experimental revolution.
Unsupervised Learning: Clustering & Dimensionality Reduction
Log in to Mediaspace to watch this video
Introduces unsupervised learning through clustering with K-means and dimensionality reduction using PCA, along with practical examples.
PyTorch and Convolutional Networks
Log in to Mediaspace to watch this video
Covers PyTorch tensor data structure and training a CNN to classify images.
Neuroscience and AI: Bridging the Gap
Log in to Mediaspace to watch this video
Explores the gap between AI and human intelligence through neuroscience-inspired models and algorithms.
SGD and Mean Field Analysis
Log in to Mediaspace to watch this video
Explores Stochastic Gradient Descent and Mean Field Analysis in two-layer neural networks, emphasizing their iterative processes and mathematical foundations.
Neural Networks: Training and Optimization
Log in to Mediaspace to watch this video
Explores the training and optimization of neural networks, addressing challenges like non-convex loss functions and local minima.
Brain-Computer Interfaces: Advancements in Systems Neuroscience
Log in to Mediaspace to watch this video
Covers brain-computer interfaces and their impact on systems neuroscience and neuroprosthetics.
Introduction to Machine Learning
Log in to Mediaspace to watch this video
Covers the basics of machine learning, including supervised and unsupervised learning, linear regression, and classification.
Previous
Page 2 of 2
Next